Method and system for controlling content distribution, related network and computer program product therefor

ABSTRACT

A method for controlling distribution of media contents over a network, wherein the contents are distributed by making the contents available at surrogate servers. The method includes the steps of identifying additional contents eligible for distribution; defining a set of categories; identifying for each category at least a reference content; associating the additional contents with the predefined categories based on semantics affinity with the reference content, the semantics affinity being calculated as the distance of each of the additional contents to the at least a reference content; selecting at least one of said predefined categories; and making at least one of the additional contents associated with said selected predefined category available for distribution at the surrogate servers.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. application Ser.No. 10/594,310, filed Sep. 27, 2006, which is a national phaseapplication based on PCT/EP2004/003409, filed Mar. 31, 2004, and thecontents both are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to techniques providingreal-time automatized content distribution, and particularly to a systemfor distributing contents over a Content Delivery Network (CDN) within aService Provider (SP) environment.

DESCRIPTION OF THE RELATED ART

In Content Delivery Networks, all the authorized requests for a specificInternet content from a particular geographic area (or PoP) arere-directed toward surrogate servers or cache servers able to providethe required content under the best conditions.

The purposes of these surrogate servers or cache servers are:

-   -   verifying that the content requested is available (i.e. stored        in the cache), updated (“fresh”) and suitable to be provided to        any requesting user; and    -   if the content requested is not available, to request it from an        original server and to store (cache) it for a following incoming        request.

A Service Provider equipped or using a Content Delivery Network isrequired to provide a content networking service to its customers.Depending on the business model involved, these customers can be ContentProviders, end users or both. The end users are the final users thatrequest content from their players or browsers.

Resorting to a Content Delivery Network becomes an inescapable need whenthe contents requested from the end users become very “significant” interms of size and/or bit-rate. This is particularly true for real-timestreaming services (both live or on-demand).

In WO-A-00/42519, a system and a method are described for acceleratingdistribution of content of a global communication network as theInternet. A central proxy server transmits extracted data files over acommunication medium to provide content filling of local proxy servers.The local proxy servers concurrently receive the data files from thecommunication medium at a high rate of speed and store the data files inattendant local cache databases. The local proxy servers utilize alocalized heuristics scheme to determine whether to keep or discard thedata. When a user requests Internet data, the user's request is firstreceived by the local proxy server. If the requested data is presentamong the cached data files, it is rapidly transmitted directly to theuser from the local proxy server; if not present, a request istransmitted to the central proxy server.

A technique as described in WO-A-01/82023 for centralized anddifferentiated content and application delivery allows content providersto directly control the delivery of content based on regional andtemporal preferences, client identity and content priority. A scalablesystem is provided in an extensible framework for edge services,employing a combination of a flexible profile definition language and anopen edge server architecture in order to add new and unforeseenservices on demand. In one or more edge servers content providers areallocated dedicated resources, which are not affected by the demand orthe delivery characteristics of other content providers. Each contentprovider can differentiate different local delivery resources within itsglobal allocation. Since the per-site resources are guaranteed,intra-site differentiation can be guaranteed. Administrative resourcesare provided to dynamically adjust service policies of the edge servers.

Additionally, WO-A-02/071191 describes a distribution arrangement drivenby a so-called metadata enabled push-pull model for efficientlow-latency video-content distribution over a network. Metadata is usedas a vehicle and mechanism to enable intelligent decisions to be made oncontent distribution system operation. Metadata is data that containsinformation about the actual content, and in some cases, the metadatamay also contain portions of the content or a low-resolution preview ofthe content. Aspects of this prior art arrangement are directed towardthe distribution of metadata throughout the network in a way thatfacilitates efficient system operation as well as optionally providingsets of services such as tracking, reporting, personalization and thelike.

OBJECT AND SUMMARY OF THE INVENTION

The object of the invention is to provide arrangements wherein the timeinvolved in responding to users' request for the contents offered byService Providers/Content Providers is further reduced by making theseService Providers/Content Providers able to make new contents availableat optimised geographic locations by forecasting the development ofrequests.

Additionally, the invention aims at permitting contents that are lapsedto be “refreshed”, by further expanding the objects available at a givencache based on self-adaptive criteria related to the trends of users'interests by relying on the semantic affinity of the content itself.

The present invention aims at achieving the goals set out in theforegoing without having to resort to a very complex architecture, whilealso developing an inferential/adaptive mechanism for automaticallymodifying the distribution policies.

According to an aspect of the present invention, such an object isachieved by means of a method for controlling distribution of mediacontents over a network, wherein said contents are distributed by makingsaid contents available at surrogate servers, the method including thesteps of:

-   -   identifying additional contents eligible for distribution;    -   defining a set of categories;    -   identifying for each category at least a reference content;    -   associating said additional contents to said predefined        categories based on semantics affinity with said reference        content, said semantics affinity being calculated as the        distance of each of said additional contents to said at least a        reference content;    -   selecting at least one of said predefined categories; and    -   making at least one of the additional contents associated to        said selected predefined category available for distribution at        said surrogate servers.

According to another aspect of the present invention such an object isachieved by means of a further method for controlling distribution ofmedia contents over a network, including a set of surrogate servers fordistributing said contents, by making said contents available at saidsurrogate servers, said method including the steps of:

-   -   receiving input information comprising at least usage        information provided by said surrogate servers, category        information provided by a first database storing a        classification in predefined categories of said distributed        contents, and a predefined interest threshold, said predefined        interest threshold being representative at least of a frequency        of the request for a given content belonging to a given        category;    -   matching with each other said input information so as to        generate a class template comprising said input information,        when said predefined interest threshold is exceeded;    -   adding to said class template content information provided by a        second database storing a classification in said predefined        categories of additional contents, said content information        including at least information on an additional content included        in said given category; and    -   generating control signals from said modified class template,        said control signals being able to control a distribution system        in order to make available said at least an additional content        at said surrogate servers.

According to a further aspect of the present invention such an object isachieved by means of a system for controlling distribution of mediacontents over a network, including a set of surrogate servers fordistributing said contents, by making said contents available at saidsurrogate servers, said system including at least:

-   -   a class matcher module configured for:        -   receiving as input information at least usage information            provided by said surrogate servers, category information            provided by a first database storing a classification in            predefined categories of said distributed contents, and a            predefined interest threshold, said predefined interest            threshold being representative at least of a frequency of            the request for a given content belonging to a given            category;        -   matching with each other said input information so as to            generate a class template comprising said input information,            when said predefined interest threshold is exceeded;    -   a class/policy template repository having a first input for        receiving said class template and a second input enabling said        operator to add to said class template content information        provided by a second database, storing a classification in said        predefined categories of additional contents, said content        information including at least information on an additional        content included in said given category; and    -   a command generator to generate control signals from said        modified class template, said control signals being able to        control a distribution system in order to make available said at        least an additional content at said surrogate servers.

According to further aspects of the present invention such an object isachieved by means of a network including such a system, and a computerprogram product loadable into the memory of at least one computer andincluding software code portions for performing the steps of the methodof the invention. As used herein, reference to such a computer programproduct is intended to be equivalent to reference to a computer-readablemedium containing instructions for controlling a computer system tocoordinate the performance of the method of the invention. Reference to“at least one computer” is evidently intended to highlight thepossibility for the present invention to be implemented in a distributedfashion.

Further preferred aspects of the present invention are described in thedependent claims and in the following description.

Essentially, the arrangement described herein provides real-timeautomatized content distribution based on a “semantics” extension ofcontents already distributed. Specifically the arrangement describedherein is based on the principle of classifying, e.g. by resorting todata mining/artificial intelligence mechanisms, users' requests (andpossibly also the actual delivery and usage of such contents) byaggregating such information with other information such as real-timeinformation concerning each and every content distributed (for instance,the share for a content in a given geographic area, the trend ofrequests during a given time period, data on users requesting a givencontent, statistics concerning those contents most frequently requested,specific information concerning the most requested content from a givenproxy-cache). The results are exploited by a Service Providers/ContentProviders to increase the contents available in those areas and in thoseperiods of time where they are actually required.

BRIEF DESCRIPTION OF THE ANNEXED DRAWINGS

The invention will now be described, by way of example only, byreferring to the enclosed figures of drawing, wherein:

FIG. 1 is a block diagram of a system implementing the presentinvention;

FIG. 2 is another block diagram detailing the structure of one of theblocks shown in FIG. 1;

FIG. 3 is a block diagram detailing the structure of another of theblocks shown in FIG. 1;

FIG. 4 is representative of the generic structure of a policy templateas established by Service Provider/Content Provider in a system asdescribed herein;

FIGS. 5, 6 and 7 are exemplary of class templates within the frameworkof the system described herein; and

FIGS. 8 and 9 are two further block diagrams showing an exemplary systemrelated to certain aspects of the present invention.

DETAILED DESCRIPTION OF AN EXEMPLARY EMBODIMENT OF THE INVENTION

FIGS. 1 and 2 show a Content Delivery Network (CDN) 1, operating over anIP network and comprising a control system E for controlling thedistribution of contents over the CDN 1.

The control system E is able to collect, filter and aggregate inputinformation a, c₁, . . . , c_(n), d, h, s₁, s₂, I respectively providedby a plurality of data sources A, C₁, . . . , C_(n), D, H, S included inthe CDN 1 and to produce output signals Out, r.

More specifically, data source A is a repository storing personalinformation, designated with a, regarding the CDN users. Typically, therepository A includes a relational database cooperating with an AAA(Authentication, Authorization and Accounting) server (such as RADIUSserver, for instance).

Data sources C₁, . . . , C_(n) are cache servers distributed over thewhole CDN 1. Each cache server C₁, . . . , C_(n) provides usageinformation, designated with c₁, . . . , c_(n), concerning each andevery content distributed. This usage information are related to, e.g.:

-   -   the share for a content in a given geographic area (the PoP        area);    -   the trend of requests during a given time period;    -   data on users requesting a given content;    -   statistics concerning those contents most frequently requested;    -   specific information concerning the most requested content from        a given cache server; and    -   meta-data for any specific requested contents.

Data source D is a commercial or generally known distribution systemproviding the control system E with the input information, designatedwith d. Typically, input information d include information regarding theCDN topology and the provisioning policies established by the ServiceProvider or the Content Providers.

Data source H is another repository including geographic information,designated with h, related to the geographic locations where usersaccess the CDN 1. For instance, when access to the CDN 1 is (also) froma mobile (GSM/GPRS/EDGE/UMTS) or wireless network, geographicinformation h comprise geometric information related to the area of thelocal register (VLR) where the users are at the moment logged in.

Input information, designated with I, are related to interactions withthe Service Provider/Content Provider (designated with the term“operator” in the following). These interactions comprise the provisionof policy templates CPT defined by the operator and described in moredetail in the following.

Data source S is a further repository including a first and a seconddatabase, designated S₁ and S₂ respectively. Repository S providescategory information, designated with s₁, s₂, related to aclassification in categories, so-called meta-families mf, of thecontents managed by the CDN 1. Each meta-family mf comprises contentshaving a certain degree of semantic affinity, as will be described inmore detail in the following. These meta-families are established by theoperator or by the Content Provider and can be of a wider type (widecardinality), for instance: sport, health education or narrower (lowcardinality), for instance: soccer, basketball, and so on.

Specifically, database S₁ contains a classification in meta-families ofthe contents already managed by the distribution system D.

Conversely, database S₂ contains classification in meta-families forother contents that are potentially suitable to be distributed.

The operator generally knows what contents have been already distributedby the distribution system D and what contents are suitable to bepotentially distributed.

FIG. 3 describes a possible implementation of a content processingsystem PM (showed also in FIG. 1) for loading category information s₁,s₂ into the databases S₁ and S₂ respectively.

More specifically, the content processing system PM comprises a semanticextraction module Z actuated in such a way to:

receive as inputs:

-   -   contents w₁ belonging to the contents already distributed by the        distribution system D;    -   contents w₂ that are suitable to be potentially distributed;    -   the meta-families mf;    -   a training set of reference contents RC, labeled for each        meta-family mf by using search engines or technical experts or        the direct experience of final users and/or a central reference        content CRC (so-called “centroid”), i.e., the meta data of a        virtual content that can suitably represents the contents of a        meta-family, obtained by the set of reference contents RC by        using data-mining techniques such as Cluster Detection etc. In        order to identify the contents w₁, w₂, the operator defines the        URLs concerning the contents distributed via the CDN 1 (this is        already a current practice in Content Delivery Networks of a        known type) and those URLs concerning the contents to be        potentially distributed;    -   classify each content w₁, w₂ in at least one meta-family mf on        the basis of the semantic affinity of the content w₁, w₂ with        the set of reference contents RC or the central reference        content CRC. This semantic affinity is calculated as the        distance, expressed by using data mining/artificial intelligence        mechanisms (such as, for example, neural networks, fuzzy logic,        decision trees), of the content w₁, w₂ from the set of reference        contents RC or the central reference content CRC; and    -   store the classification in meta-families of the contents        already distributed w1 in the first database S₁ and store the        classification in meta-families of those contents suitable to be        potentially distributed w₂ in the second database S₂.

The structure of the databases S₁, S₂ involve collecting for eachmeta-family all the contents found by the semantic extraction module Z,possibly admitting superposition of meta-families and hierarchies.

Again with reference to FIGS. 1 and 2, output signals Out comprisecontent distribution events/actions based on triggered policies(“designated with the term “class templates” in the following) that aredirectly forwarded to the Content Provider while output signals rcomprise class templates intended to drive the distribution system D inorder to implement the necessary action for distributing the contents orfor modifying the distribution policies. The action of distributing thecontents is designated with p.

The block diagram of FIG. 2 details the structure and operation of thecontrol system E. The control system E includes a class matcher moduleE1, a class/policy template repository E2, a command generator E3 and anAPI interface module E4.

Specifically, the class matcher module E1 is configured for matchingtogether input information, selected between the information a, c₁, . .. , c_(n), d, h, s₁ respectively provided by the data sources A, C₁, . .. , C_(n), D, H, S₁, and feedback information, designated with e₂ inFIG. 2, provided by the class/policy template repository E2. Then, theclass matcher module E1 generates a class template, designated CL, whenan interest threshold is exceeded. The data sources from which the classmatcher module E1 receives the input information and the interestthreshold are defined by the operator in the policy template CPT asdescribed in the following. Moreover, the class matcher module E1 usesthe feedback information, provided by the class/policy templaterepository E2, in order to optimize the number of class templatesgenerated and to forward the operator customizations about e.g. thethresholds.

In a preferred embodiment of the present invention, the key inputinformation of the class matcher module E1 are represented by the usageinformation from the caches C₁, . . . , C_(n), and the policy templateCPT defined by the operator. Then the class matcher module E1 matchestogether these information in a class template CL only if the interestthreshold is reached. In a preferred embodiment of the present inventionsuch an interest threshold can be representative of the frequency of therequest for a given content belonging to a given meta-family extractedby the first database S₁. Subsequently, the class matcher module E1 canalso filter and aggregate other input information (on the basis of thepolicies defined by the operator in the policy template CPT) in order totrigger the class template CL with all the required information.

The class template CL obtained is then forwarded to the class/policytemplate repository E2. Essentially, this is a database with a remoteaccess configuration (designated with I) enabling the operator to inputselected configuration policies (“actions”) that are added to the classtemplate CL generated by the class matcher module E1, as will bedescribed in more detail in the following. For example the ServiceProvider may decide to distribute directly certain distribution eventsscheduled by the class template CL rather than directly informing theContent Provider by requesting to the Content Provider a confirmation(e.g. as a video display).

Furthermore, the operator can access the class/policy templaterepository E2 and, based on the meta-family extracted by the classmatcher module E1, define what contents among those suitable to bepotentially distributed (w₂) and belonging to the extracted meta-family,can be inserted in the class CL. In that way, the distribution ofcontents already provided to the users via the CDN 1 can be optimised,while meeting the interests voiced by the users by distributing contentshaving similar semantic characteristics.

The class template CL suitably modified by the operator (“modified classCL_(m)”) is then sent to a command generator E3 to generate therefromcontrol signals r′ suitable to be forwarded toward the distributionsystem D and the output signals out suitable to be forwarded toward theContent Provider involved.

Preferably, the control signals r′ are processed via an API interfacemodule E4 for producing the output signals r suitable to drive thedistribution system D. Specifically, this API module E4 provides theprogramming interface for content distribution (for instance, byproviding actions such as distribute, remove, enquiry, notify).

FIG. 4 details the structure of a policy template CPT defined by theoperator.

The policy template CPT in question essentially includes three portionsdesignated 11, 12 and 13. The first and the second portion 11, 12 areassigned to the class matcher module E1 while the third portion 13 isassigned to the class/policy template repository E2.

Specifically, in the first portion 11 (“context portion 11”), theoperator defines the policy template context. In detail, this firstportion 11 can include parameters that define which information are madeavailable to the Content Provider by the Service Provider, more inparticular, which information is to be collected from the data sourcesA, C₁, . . . , C_(n), D, H, S. Typically this information relates tocontent reference, hosted domain, semantics, user personal data, networktopology, user location etc. Advantageously, the first portion 11 allowsto optimise collection of information by the data sources while avoidingto repeat it for each context by using the feedback information e₂.Within the first portion 11, the Content Provider may also include somerelevant clauses, as better detailed in the following with reference toFIGS. 5, 6 and 7.

In the second portion 12 (“threshold portion 12”), the operator sets theinterest threshold through which the contents extracted from the contextcan be filtered, as will be described in more detail in the following.

The third portion 13 (“action portion 13”), assigned to the class/policytemplate repository E2, includes information related to the actions(such as distribute/remove/modify) associated with the class triggering.Such actions can be executed automatically or submitted to the operatorto obtain a confirmation.

Subdividing the policy template CPT in three portions leads to tasks andconfiguration objects being defined between the Service Provider and theContent Provider in a flexible manner depending on the specific needs.

For example in FIG. 5 is shown an example of how distribution can beextended to other hosted domains based on a certain threshold conditionbeing met by reference to contents that are semantically analogous andthat were distributed previously.

In this case, it will be assumed that the context of interest for theinvolved Content Provider relates the contents of a domain (e.g.“www.rai1.it”) of a TV company related to the “soccer” semantic, suchcontents being distributed in the caches in a given geographical area,for instance, northern Italy, (see context portion 11).

The Content Provider can use a context clause to limit screening of thepossible uses by the systems.

The interest threshold, as defined, is expressed in the thresholdportion 12 and verifies if e.g. the semantic “soccer” in the context ofconcern had a number of requests higher than, say, 50 within the timewindow of the last thirty minutes.

Finally, the operation proposed for the context as defined in thecontext portion 11 is applied in the action portion 13 to thedistribution system D (“distribute”) while being extended to one or moreadditional hosted domain that include contents having a degree ofsemantic affinity with the contents of the domain selected in thecontext portion 11, e.g. “www.rai2.it” and “www.rai3.it”.

The three respective blocks are designated 111, 112 and 113.

FIG. 6 shows another example of extension of distribution. This case,however, implements a more general, indirect mechanism by exploitingsome macro-definitions at the policy template level.

Specifically, in the context portion 11, the operator specifies only thehosted domain, by leaving to the system the task of performing acomplete screening in order to detect the semantics of all the contentsdistributed and requested for the hosted domain in question.

In the threshold portion 12, the interest threshold is specified in anindirect manner by referring to the element “semantics” as a list ofsemantics detected in the usages. This permits the parametrization alsofor the action portion 13 by providing dynamically the list of semanticsfor which distribution will have to be extended. At the moment ofdistribution, the system will take care of detailing the semanticreferences in the various content references related thereto.

Specifically, by referring to FIG. 6, a block 211 designates the actionof retrieving the semantics for all the contents distributed within agiven domain.

A block 212 denotes the action of detecting what semantics, among thosefound, are the object of a number of request higher than, say, fiftyduring the last thirty minutes.

Finally, block 213 refers to the possible distribution of contents withthe same semantics of the domain thresholds to contents that are notdistributed yet.

The example of FIG. 7 refers to a situation where direct reference tothe contents is exploited in evaluating the threshold, in order toactivate in an automatic way a “cleaning” algorithm directed at thecaches in an aimed manner.

Specifically, a block 311 designates a step wherein all the contentsdistributed within a given domain are located, while a block 312designates a step wherein those contents having no accesses during thelast three days are detected.

Finally, the block 313 designates a step wherein the contents thusdetected are removed.

FIGS. 8 and 9 show an example of operation of the CDN 1, according tothe present invention.

With reference to FIG. 8, the class matcher module E1 receives as inputs(defined by the operator in the context portion 11 of the policytemplate CPT):

-   -   cache log information derived from the cache servers C₁, . . . ,        C_(n) about, for example, user requests for a particular domain        related to a soccer team during Sunday afternoon (block 400);    -   personal information derived from AAA servers about the users        requesting this content. These personal information can show        that the persons requesting this domain are on the average men        20 to 40 years of age with an ADSL access (block 401);    -   semantic information about the categorisations comprising the        distributed contents (block 402).    -   network topology information (block 403) showing that the        Content Provider “owning” this content is, for example, a given        broadcasting corporation. This information is typically derived        from the CDN Management system.

When an interest threshold, defined by the operator in the thresholdportion 12 of the policy template CPT, is exceeded the class matcher E1generates a class template n. The interest threshold can berepresentative, for example, of the frequency of user requests for agiven content belonging to a given category (that is a given semantics).The generated class template n is marked e.g. as a “Very FrequentlyRequested” content class for the given Content Provider for a specificsemantic. The relevant elements of the class template n are denoted withnumbers from 0 to 3:

-   -   time: Sunday afternoon (0)    -   Content Provider: www.rai.it (1),    -   cache: Milan (2),    -   type of public: 20-40 old (3)).

FIG. 9 can be essentially regarded as a continuation of FIG. 8.

FIG. 9 shows how the class template n generated in the foregoing andanother class template m, generated during a given time range, are sentto the class/policy template repository E2 in order to verify respectivetriggering events for the policies.

In the examples shown, the class m is a “frequently requested content”class for the same Content Provider e.g. www.rai.it.

Through the class/policy template repository E2, the operator is able toconfigure the distribution system D by managing in real-time the classtemplates n, m depending on defined polices (actions contain in theaction portion 13).

Then, the command generator E3 receives from the class/policy templaterepository E2 the modified class templates n, m comprising theinstructions (actions) related to managing such class templates.

At this point, the command generator E3 is able to deliver specificcommands towards the distribution system D of the CDN 1.

The API interface E4 provides the necessary communication between thecontrol system E and the distribution system D.

In the specific example shown, the control system E instantiatesdistribution commands for similar contents belonging to the samecategory used as an input.

By assuming that the Content Provider has a given content potentiallyavailable for distribution and belonging to the same category, thecontrol system E may consider it for insertion in view of distributionand handle it with the same policies defined for the class templateinstantiated.

Furthermore, in defining policies it is possible to use also referencesto semantics or contents that are not directly used in the definition ofthe class template. This allows the operator i.a. to triggerdistribution events starting from a content semantics while distributinganother one. For instance, it is possible to define within a class thesemantic “soccer match with the team A” and activate distribution byexploiting the same policies for the semantics “results for the firstdivision in the soccer championship”.

The arrangement described herein is particularly advantageous when aneed exists of:

-   -   responding quickly to request from users, by trying to predict        the behaviour of requests, and    -   optimising exploitation of the storage resources of the CDN        apparatuses, which is particularly important when the contents        have considerable sizes.

In fact, this arrangement provides a viable response to these needs, bypermitting contents to be distributed in an intelligent manner by takinginto account usage data. This while also allowing the operator toconsider, based on specific classifications, significant parameters suchas any periodicity in the requests detected, the geographic andsocio-anagraphic significance of the requests as well as the semanticsof the contents available for distribution. The distribution policyderived from this classification permits to load/fill into specificdevices in a CDN those contents held to represent the most significantcontents based on such parameters, while also possibly dispensing withthose contents held to attract a lower degree of interest.

Furthermore, the arrangement described herein is suitable to maintaincomplete real-time documentation concerning the requests expressed forall the contents distributed over a whole CDN 1. This documentationprovides the management of the network with significant amount of areal-time information concerning each and every content distributed soas to produce suitable control signals for the distribution system D.Consequently, the CDN 1 may be operated by implementing distributionpolicies of the contents defined as a real-time policies.

Of course, without prejudice to the underlying principle of theinvention, the details and embodiments may vary, also significantly,with respect to what has been described and shown, by way of exampleonly, without departing from the scope of the invention, as defined inthe claims that follow.

The invention claimed is:
 1. A system for distributing media contentsover a network of an operator, comprising a set of surrogate servers fordistributing said contents, by making said contents available at saidsurrogate servers, said system comprising at least: a semantic extractedmodule configured for: receiving as inputs at least distributedcontents, additional contents that are not distributed over the networkyet, predefined categories, and a reference content identified for eachcategory, said identification of said reference content comprisingidentifying a set of reference contents by using search engines andcalculating a central reference content of said set of referencecontents; classifying each distributed content/additional content in atleast one category, said classification of each of said distributedcontents/additional contents being based on semantics affinity amongsaid reference content and each of said distributed content/additionalcontents, said semantics affinity being calculated as the distance ofeach of said distributed content/additional content to said at least areference content, said calculation comprising using data mining orartificial intelligence mechanisms; and storing said classification ofsaid distributed contents in a first database and said classification ofsaid additional contents in a second database; a class matcher moduleconfigured for: receiving as input information at least usageinformation provided by said surrogate servers, category informationprovided by said first database, and a predefined interest threshold,said predefined interest threshold being representative at least of afrequency of the request for a given content belonging to a givencategory; analyzing said input information to generate a class templatecomprising said input information, when said predefined interestthreshold is exceeded; a class/policy template repository having a firstinput for receiving said class template and a second input for adding tosaid class template content information provided by said seconddatabase, said content information comprising at least information on anadditional content included in said given category; and a commandgenerator to generate control signals from said modified class template,said control signals being able to control a distribution system inorder to make available said at least an additional content at saidsurrogate servers.
 2. The system according to claim 1, wherein saidusage information comprises at least a usage information selected from:the share for a content in a given geographic area; the trend ofrequests during a given time period; data on users requesting a givencontent; statistics concerning those contents most frequently requested;specific information concerning the most requested content from a givencache server; and meta-data for any specific requested contents.
 3. Anetwork comprising a set of surrogate servers for distributing mediacontents, wherein said contents are distributed by making these contentsavailable at said surrogate servers, comprising a system according toclaim
 1. 4. A method for distributing media contents over a network ofan operator, comprising a set of surrogate servers for distributing saidcontents, by making said contents available at said surrogate servers,said method comprising the steps of: receiving as inputs at leastdistributed contents, additional contents that are not distributed overthe network yet, predefined categories, and a reference contentidentified for each category, said identification of said referencecontent comprising identifying a set of reference contents by usingsearch engines and calculating a central reference content of said setof reference contents; classifying each distributed content/additionalcontent in at least one category, said classification of each of saiddistributed contents/additional contents being based on semanticsaffinity among said reference content and each of said distributedcontent/additional contents, said semantics affinity being calculated asthe distance of each of said distributed content/additional content tosaid at least a reference content, said calculation comprising usingdata mining or artificial intelligence mechanisms; storing saidclassification of said distributed contents in a first database and saidclassification of said additional contents in a second database;receiving input information comprising at least usage informationprovided by said surrogate servers, category information provided bysaid first database, and a predefined interest threshold, saidpredefined interest threshold being representative at least of afrequency of the request for a given content belonging to a givencategory; analyzing said input information so as to generate a classtemplate comprising said input information, when said predefinedinterest threshold is exceeded; adding to said class template contentinformation provided by said second database, said content informationcomprising at least information on an additional content included insaid given category; and generating control signals from said modifiedclass template, said control signals being able to control adistribution system in order to make available said at least anadditional content at said surrogate servers.
 5. A non-transitory,computer-readable medium comprising instructions for causing a computerto perform the steps of claim 4.